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Kareem Madkour

Observed name: K. Madkour
Institution: Siemens (Germany)
OpenAlex ID: A5061563066
Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders
2019 · Gaurav Rajavendra Reddy, Kareem Madkour, et al. · 43
Hotspot detection using machine learning
2016 · Kareem Madkour, Sarah Mohamed, et al. · International Symposium on Quality Electronic Design (ISQED) · 19
Enhanced OPC recipe coverage and early hotspot detection through automated layout generation and analysis
2017 · Ayman Hamouda, Mohamed Bahnas, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 15
Design space exploration for early identification of yield limiting patterns
2016 · Helen Li, Elain Zou, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 14
A random generation approach to pattern library creation for full chip lithographic simulation
2017 · Elain Zou, Sid Hong, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 7
Toward automated parasitic extraction of silicon photonics using layout physical verifications
2016 · Mohamed Ismail, Raghi S El Shamy, et al. · Journal of Optics · 7
Machine learning to improve accuracy of fast lithographic hotspot detection
2019 · Namjae Kim, KiHeung Park, et al. · Design-Process-Technology Co-optimization for Manufacturability · 5
Autoencoder-Based Data Sampling for Machine Learning-Based Lithography Hotspot Detection
2022 · Mohamed Tarek Ismail, Hossam Sharara, et al. · ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) · 3
Early stage hot spot analysis through standard cell base random pattern generation
2017 · Joong-Won Jeon, Jaewan Song, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 3
A new era DFM solution for yield enhancement using machine learning (ML)
2024 · Namjae Kim, Jae-Hyun Kang, et al. · DTCO and Computational Patterning · 2
A quantified approach of dataset selection for training ML models on hard-to-classify patterns
2021 · Mohamed Ismail, Mohamed Bahnas, et al. · SPIE Design-Process-Technology Co-optimization · 2
Approaches for full coverage physical design space exploration and analysis by synthetic layout generation
2020 · Marwah Shafee, Joe Kwan, et al. · Design-Process-Technology Co-optimization for Manufacturability · 2
A weak pattern random creation and scoring method for lithography process tuning
2018 · Xinyi Hu, Meili Zhang, et al. · Design-Process-Technology Co-optimization for Manufacturability · 2
A random approach of test macro generation for early detection of hotspots
2016 · Jong-hyun Lee, Chin Kim, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2
Pattern-based DTCO flow for early estimation of lithographic difficulty using optical image processing
2016 · Moutaz Fakhry, Kareem Madkour, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2
Unsupervised Graph Neural Network Framework for Balanced Multipatterning in Advanced Electronic Design Automation Layouts
2026 · Abdelrahman Helaly, Kareem Madkour, et al. · IEEE Access · 1
A study on the improvement of machine learning (ML)-based defect prediction model reflecting process variations
2025 · Seungsu Yoon, Namjae Kim, et al. · DTCO and Computational Patterning · 1
Litho hotspots fixing using model based algorithm
2017 · Meili Zhang, Shirui Yu, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1
A MEMS-based Coupled-cavity Tunable Optical Filter
2006 · M. Gheith, A. Hussein, et al. · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · 1
Guided dataset enrichment for robust process modeling: from random exploration to intelligent feature-aware pattern generation
2026 · Waleed Osama, Emily Thomas, et al. · DTCO and Computational Patterning
IP Session on Machine Learning Applications in IC Test-Related Tasks
2019 · Ghada Sokar, Yassien Zakaria, et al. · IEEE VLSI Test Symposium (VTS)
Innovative practices on machine learning for emerging applications
2018 · Kareem Madkour, Zhaobo Zhang, et al. · IEEE VLSI Test Symposium (VTS)
Analytical parasitic extraction for fast physical verification of silicon photonics
2016 · Raghi S. El Shamy, Mohamed Ismail, et al. · Photonics North (PN)
DFM Techniques for the Detection and Mitigation of Hotspots in Nanometer Technology
2015 · Kareem Madkour · UWSpace (University of Waterloo)
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Works count: 24
Citation count: 132
H-index: 6
I10-index: 4
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